README.md

In dloewe/dukeR: Tools for analysing data from Duke University Hospital

dukeR 0.1.0

dukeR

Tools for analysing Duke University Hospital data.

Description

The idea behind this package is to gather data analysis tools for the R statistical system to help facilitate collaboration when analyzing data from Duke Univeristy Hospital.

Disclaimer

This package is not officially affiliated with Duke University Hospital. The author assumes no responsibility or liability for the use of the package.

Installation

If not installed, install devtools package.

install.packages("devtools")

Continue on to install dukeR

devtools::install_github("dloewenstein/dukeR")

Done.

Usage

Analysing ecg .xml files

Below follows an example of how to read xml files from a folder and merge them to a dataframe, which if necessary can be exported to a .csv or .xslx file.

Load the package.

require("dukeR")

Create a variable holding paths to the xml files.

xml_files <- list.files("./your_xml_folder", full.names = TRUE)

If you only have one .xml file then you could directly apply the dr_read_ecgxml() function. However, most probably you have multiple .xml files that you want to read in which case you can make use of the map() function from the purrr package.

ecg_list <- purrr::map(xml_files, dr_read_ecgxml)

This will result in a list of dataframes, one for each ecg. If reading a large number of ecgs it's recommended to make use of the data.table package. Below follows an example of how to merge the list of dataframes to one.

ecg_data <- data.table::rbindlist(ecg_list, fill = TRUE)

You now have a dataframe (or rather a data.table) suitable for analysis, however dukeR also contains the function dr_classify_ecg() which will perform a simple form of text analysis on the diagnosis statements using regex expressions and in combination with QRS duration criteria classify the ecg according to the following: - Sinus rhytm, - Supra ventricular tachycardia (SVT), - Junctional rhythm, - Left bundle branch block (LBBB), - Right bundle branch block (RBBB), - RBBB + left anterior fascicular block (LAFB), - RBBB + left posterior fascicular block (LPFB), - Atrial fibrillation (AFIB), - Pacemaker rhythm (PACE), - Wolff-Parkinson-White (WPW).

ecg_classified <- dr_classify_ecg(ecg_data)

You have just finished analysing your ecgs, congratulations.

dloewe/dukeR documentation built on May 14, 2019, 10:33 a.m.